In real-world applications, many complex problems can be formulated as mathematical optimization challenges, and efficiently solving these problems is critical. Metaheuristic algorithms have proven highly effective in addressing a wide range of engineering issues. The differentiated creative search is a recently proposed evolution-based meta-heuristic algorithm with certain advantages. However, it also has limitations, including weakened population diversity, reduced search efficiency, and hindrance of comprehensive exploration of the solution space. To address the shortcomings of the DCS algorithm, this paper proposes a multi-strategy differentiated creative search (MSDCS) based on the collaborative development mechanism and population evaluation strategy. First, this paper proposes a collaborative development mechanism that organically integrates the estimation distribution algorithm and DCS to compensate for the shortcomings of the DCS algorithm’s insufficient exploration ability and its tendency to fall into local optimums through the guiding effect of dominant populations, and to improve the quality of the DCS algorithm’s search efficiency and solution at the same time. Secondly, a new population evaluation strategy is proposed to realize the coordinated transition between exploitation and exploration through the comprehensive evaluation of fitness and distance. Finally, a linear population size reduction strategy is incorporated into DCS, which significantly improves the overall performance of the algorithm by maintaining a large population size at the initial stage to enhance the exploration capability and extensive search of the solution space, and then gradually decreasing the population size at the later stage to enhance the exploitation capability. A series of validations was conducted on the CEC2018 test set, and the experimental results were analyzed using the Friedman test and Wilcoxon rank sum test. The results show the superior performance of MSDCS in terms of convergence speed, stability, and global optimization. In addition, MSDCS is successfully applied to several engineering constrained optimization problems. In all cases, MSDCS outperforms the basic DCS algorithm with fast convergence and strong robustness, emphasizing its superior efficacy in practical applications.

The transformation and upgrading of the construction industry has made obvious progress in China. However, the low degree of digitalization is still prominent. The digital transformation decision-making behavior of key participants in the construction industry is easily affected by economic interests, meaning that the implementation of digital transformation is a dynamic evolution process of repeated adjustment and continuous learning. Therefore, this study constructed a tripartite evolutionary game model of the government, service providers, and construction enterprises, and analyzed the strategy selection of each game player and the stability of the system equilibrium point. Moreover, the validity of the model and the influence of various factors on the system were verified and analyzed by numerical simulation. The results showed the following: (1) The government’s tax refund ratio and subsidy coefficient can significantly increase the motivation of construction enterprises and digital construction service providers within a certain threshold. (2) Whether enterprises carry out a comprehensive digital transformation depends on the revenue of the system solution strategy; the greater the revenue, the more positive the enterprises. (3) The adoption of high rewards and high penalties by the superior government can effectively encourage the local government to actively promote digital transformation. The contribution of this study lies in providing a theoretical basis and decision support for promoting the digital transformation of construction enterprises.

In recent years, public health emergencies have severely disrupted city functions and endangered residents’ health and lives, enhancing the emergency response capacity, a crucial aspect of building resilient cities. Based on the Wuhan Statistical Yearbook and local economic and social big data, this study constructed a resilience assessment system that covers resistance, adaptability, and resilience. Evaluate the resilience level of each region and analyze its spatiotemporal characteristics using the entropy weight method, Theil index, and natural breakpoint method. The central area exhibited higher resilience levels, while peripheral areas showed lower resilience, owing to location advantage and developmental disparities. The obstacle degree model revealed that scientific and technological innovation, social rescue capabilities, and population size are the primary weak links in building urban emergency response capacity. Based on these findings, this study aims to provide policy recommendations and improvement measures for multiple stakeholders, guide the construction of resilient cities, enhance the ability to respond to public health crises and recovery speed, and ensure urban functions and residents’ well-being.

Engineering activities can sometimes be affected by individual risk factors, but they can also be affected by multiple risk factors combined. However, existing studies rarely examine whether multiple risk factors superimposed on a particular activity result in superposed or non-superposed effects. To some extent, these effects may influence the accuracy of project managers’ decision-making when handling risks. By introducing the Bayesian Network (BN) into the process of multi-factor superposition influence analysis of project schedule uncertainty, this research expounded on the construction and implementation of the Bayesian Network diagram of project schedule risk. Based on the Bayesian Network concept, this research developed a Bayesian network-based engineering schedule risk detection model. Further, it was examined whether the influence produced by the superposition of risk factors equals the sum of the influences produced by each risk factor acting alone. It has been demonstrated through engineering examples that the model does not only clearly express project schedules but also has all the functions of a Bayesian Network, allowing it to serve as a platform for processing uncertain data. The case also revealed that when multiple risk factors are superimposed, the impact on the project schedule is not superimposed. These findings provide policymakers with a more comprehensive understanding of how to respond to risk.

With climate change and urbanization accelerating, cities face increasing disaster risks. As critical facilities for safeguarding residents’ safety, emergency shelters have attracted growing attention due to spatial supply–demand mismatches. From the perspective of the 15-min community life circle, this study aims to develop a fine-scale analytical framework for assessing emergency shelter accessibility and inequality, using Pidu District, Chengdu, China, as a case study. The three-step floating catchment area (3SFCA) method is applied to assess emergency shelter accessibility, while the Gini coefficient and Kolm–Pollak equally distributed equivalent (EDE) are introduced to evaluate accessibility inequality across subdistricts and communities. The results show that: (1) compared with the traditional Gaussian two-step floating catchment area (G2SFCA) method, 3SFCA captures clearer accessibility differentiation and stronger local sensitivity; (2) emergency shelter accessibility shows marked spatial differentiation, with pronounced local clustering in the walking mode and a polycentric pattern in the cycling mode; (3) the Gini coefficient reveals distributional imbalance in emergency shelter accessibility, showing a clear “center–periphery” pattern in which urban areas generally exhibit lower inequality than suburban and rural areas; and (4) the Kolm–Pollak EDE emphasizes lower-tail welfare and helps distinguish “low but even” areas from “high but polarized” areas. These findings support equity-oriented strategies for emergency shelter planning and resource allocation. The study provides methodological and practical implications for fine-scale emergency shelter planning and resilient urban governance, contributing to more equitable, inclusive, resilient, and sustainable urban environments.